%\include {preamble}

%\begin{document}

%\subsection{Comparison with semi supervised SVM}
%\frame { \frametitle {Compared with Metric Learning Approach}

%}

\frame { \frametitle {Comparison with semi supervised SVM}
  Results on classifying the unlabelled target data under two experimental settings:
  \begin {itemize}
    \item Fixing the number of labeled samples from source domain and varying the labeled samples from target.
    \item Fixing the number of labeled target data and varying the labeled samples from source.
  \end {itemize}
}

\frame { \frametitle {}
  \begin{figure}
    \includegraphics[width = 0.95 \textwidth] {fig/exp_svm}
  \end{figure}
}

%\subsection{Studying the information conveyed by intermediate subspaces, and multi-domain adaptation}


\frame { \frametitle {The numbers of intermediate subspaces}
  \begin{figure}
    \includegraphics[height = 0.5 \textheight] {fig/exp_subspace_num}
  \end{figure}
  \begin {itemize}
   \tiny
    \item     Accuracy increases when there are intermediate subspaces ($N' > 2$), which means the intermediate subspaces provide useful information.
    \item More intermediate subspaces not always provides more useful information (e.g. Bing/Caltech curve)
  \end {itemize}
}

%\frame { \frametitle {Multi-domain Adaptation}
%To test the multi-domain adaption, we create six different cases, three with two source domains and one target domain, and the other three with one source domain and two target domains.
%}
%
%\frame { \frametitle {Multi-domain Adaptation}
%
%  \begin{figure}
%    \includegraphics[width = 0.95 \textwidth] {fig/table2}
%  \end{figure}
%
%  \begin {itemize}
%    \item For the case where the target domain is \textit{webcam} and the source domains contain \textit{dslr} and \textit{amazon}, the joint source adaption results lies between single source domain cases.
%  \end {itemize}
%}

\frame { \frametitle {Comparison with unsupervised approaches on non-visual domain data}
  Experimental Setting:
  \begin{itemize}
    \item A dataset of product reviews from amazon.com for four different domains: books, DVD, electronics and kitch appliances.
    \item Each review has a rating from 0 to 5, comments, reviewer name, location
    \item Rating more than 3 were classified as positive, otherwise as negative
  \end{itemize}
  Goal: The process of learning positive/negative reviews from one domain is applicable to another domain.
  
}

\frame { \frametitle {} 
  \begin{figure}
    \includegraphics[height = 0.5 \textheight] {fig/table3}
  \end{figure}
  
  The results shows that the accuracy of our method is better than previous.
  
}




%\end{document}
